Hostname: page-component-8448b6f56d-42gr6 Total loading time: 0 Render date: 2024-04-19T07:51:18.127Z Has data issue: false hasContentIssue false
  • English
  • Français

Weed Vegetation of Sugarcane Cropping Systems of Northern Argentina: Data-Mining Methods for Assessing the Environmental and Management Effects on Species Composition

Published online by Cambridge University Press:  20 January 2017

D. O. Ferraro ,
C. M. Ghersa  and
D. E. Rivero
D. O. Ferraro*
Affiliation:
IFEVA, Cátedra de Cerealicultura, Facultad de Agronomía, Universidad de Buenos Aires/CONICET, Av. San Martín 4453, Buenos Aires (1417DSE), Argentina
C. M. Ghersa
Affiliation:
IFEVA, Cátedra de Ecología. Facultad de Agronomía, Universidad de Buenos Aires/CONICET
D. E. Rivero
Affiliation:
IFEVA, Cátedra de Ecología. Facultad de Agronomía, Universidad de Buenos Aires/CONICET
*
Corresponding author's E-mail: ferraro@agro.edu.ar
Get access Rights & Permissions [Opens in a new window]

Abstract

Weed composition may vary because of natural environment, management practices, and their interactions. In this study we presented a systematic approach for analyzing the relative importance of environmental and management factors on weed composition of the most conspicuous species in sugarcane. A data-mining approach represented by k-means cluster and classification and regression trees (CART) were used for analyzing the 11 most frequent weeds recorded in sugarcane cropping systems of northern Argentina. Data of weed abundance and explanatory factors contained records from 1976 sugarcane fields over 2 consecutive years. The k-means method selected five different weed clusters. One cluster contained 44% of the data and exhibited the lowest overall weed abundance. The other four clusters were dominated by three perennial species, bermudagrass, johnsongrass, and purple nutsedge, and the annual itchgrass. The CART model was able to explain 44% of the sugarcane's weed composition variability. Four of the five clusters were represented in the terminal nodes of the final CART model. Sugarcane burning before harvesting was the first factor selected in the CART, and all nodes resulting from this split were characterized by low abundance of weeds. Regarding the predictive power of the variables, rainfall and the genotype identity were the most important predictors. These results have management implications as they indicate that the genotype identity would be a more important factor than crop age when designing sugarcane weed management. Moreover, the abiotic control of crop–weed interaction would be more related to rainfall than the environmental heterogeneity related to soil type, for example soil fertility. Although all these exploratory patterns resulting from the CART data-mining procedure should be refined, it became clear that this information may be used to develop an experimental framework to study the factors driving weed assembly.

Keywords

Bermudagrass, Cynodon dactylon Pers. (CYNDA)johnsongrass, Sorghum halepense (L.) Pers. (SORHA)purple nutsedge, Cyperus rotundus L. (CYPRO)itchgrass, Rottboellia exaltata (L.) L.f.(ROOEX)Sugarcaneweed compositionclassification and regression treesstatistics
Type
Weed Biology and Ecology
Information
Weed Science , Volume 60 , Issue 1 , March 2012 , pp. 27 - 33
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Ali, A. D., Reagan, T. E., Kitchen, L. M., and Flynn, J. L. 1986. Effects of johnsongrass (Sorghum halepense) density on sugarcane (Saccharum officinarum) yield. Weed Sci. 34:381–383.Google Scholar
Bariuan, J. V., Reddy, K. N., and Wills, G. D. 1999. Glyphosate injury, rainfastness, absorption, and translocation in purple nutsedge (Cyperus rotundus). Weed Technol. 13:112–119.Google Scholar
Basanta, M. V., Dourado-Neto, D., Reichardt, K., et al. 2003. Management effects on nitrogen recovery in a sugarcane crop grown in Brazil. Geoderma. 116:235–248.Google Scholar
Booth, B. D. and Swanton, C. J. 2002. Assembly theory applied to weed communities. Weed Sci. 50:2–13.Google Scholar
Braunbeck, O., Bauen, A., Rosillo-Calle, F., and Cortez, L. 1999. Prospects for green cane harvesting and cane residue use in Brazil. Biomass Bioenergy. 17:495–506.Google Scholar
Breiman, L., Friedman, R., Olshen, R., and Stone, C. 1984. Classification and Regression Trees. Boca Raton, FL CRC Press. 368 p.Google Scholar
Christoffoleti, P. J., de Carvalho, S.J.P., López-Ovejero, R. F., Nicolai, M., Hidalgo, E., and da Silva, J. E. 2007. Conservation of natural resources in Brazilian agriculture: implications on weed biology and management. Crop Prot. 26:383–389.Google Scholar
Debeljak, M., Squire, G. R., Demsar, D., Young, M. W., and Dzeroski, S. 2008. Relations between the oilseed rape volunteer seedbank, and soil factors, weed functional groups and geographical location in the UK. Ecol. Model. 212:138–146.Google Scholar
Ellis, R. N., Basford, K. E., Cooper, M., Leslie, J. K., and Byth, D. E. 2001. A methodology for analysis of sugarcane productivity trends. I. Analysis across districts. Aust. J. Agric. Res. 52:1001–1009.Google Scholar
Evenson, C. I., Muchow, R. C., El-Swaify, S. A., and Osgood, R. V. 1987. Yield accumulation in irrigated sugarcane. I. Effect of crop age and cultivar. Agron. J. 89:638–646.Google Scholar
Ferraro, D. O., Rivero, D. E., and Ghersa, C. M. 2009. An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees. Field Crop. Res. 112:149–157.Google Scholar
Firehun, Y. and Tamado, T. 2006. Weed flora in the Rift Valley sugarcane plantations of Ethiopia as influenced by soil types and agronomic practises. Weed Biol. Manag. 6:139–150.Google Scholar
Galdos, M., Cerri, C., Cerri, C., Paustian, K., and Van Antwerpen, R. 2010. Simulation of sugarcane residue decomposition and aboveground growth. Plant Soil. 326:243–259.Google Scholar
Garside, A. L., Smith, M. A., Chapman, L. S., Hurney, A. P., and Magarey, R. C. 1997. The yield plateau in the Australian sugar industry: 1970–1990. Pages 103–124 in Keating, B. A. and Wilson, J. R., eds. Intensive Sugarcane Production: Meeting the Challenges Beyond 2000. Wallingford, UK CAB International.Google Scholar
Garzón, M. B., Blazek, R., Neteler, M., Dios, R. S. d., Ollero, H. S., and Furlanello, C. 2006. Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecol. Model. 197:383–393.Google Scholar
Gonzalez-Andujar, J. L., Fernandez-Quintanilla, C., Izquierdo, J., and Urbano, J. M. 2006. SIMCE: an expert system for seedling weed identification in cereals. Comp. Electron. Agr. 54:115–123.Google Scholar
Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1977. The World's Worst Weeds: Distribution and Biology. Honolulu, HI University Press of Hawaii. 609 p.Google Scholar
Jain, A. K. 2010. Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31:651–666.Google Scholar
Jain, A. K. and Dubes, R. C. 1988. Algorithms for Clustering Data. New Jersey Prentice-Hall. 320 p.Google Scholar
Kang, M. S., Miller, J. D., Tai, P.Y.P., Dean, J. L., and Glaz, B. 1987. Implications of confounding of genotype × year and genotype × crop effects in sugarcane. Field Crop. Res. 15:349–355.Google Scholar
Kenkel, N. C., Derksen, D. A., Thomas, A. G., and Watson, P. R. 2002. Multivariate analysis in weed science research. Weed Sci. 50:281–292.Google Scholar
Koziol, J. A. G. 1990. Cluster analysis of antigenic profiles of tumours: selection of number of clusters using Akaike's information criterion. Method. Inform. Med. 29:200–204.Google Scholar
Kuva, M., Christoffoleti, P., and Pitelli, P. 1999. Critical period of competition between sugarcane and weeds in Brazil. Weed Sci.Soc. Am. Abstr. 25 p.Google Scholar
Kuva, M. A., Pitelli, R. A., Salgado, T. P., and Alaves, P. L. C. A. 2007. Fitossociologia de comunidades de plantas daninhas em agroecossistema cana-crua. Planta Daninha. 25:501–511.Google Scholar
Lawes, R. A., Lawn, R. J., Wegener, M. K., and Basford, K. E. 2004. The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar. Aust. J. Agric. Res. 55:335–344.Google Scholar
Liu, D. L., Kingston, G., and Bull, T. A. 1998. A new technique for determining the thermal parameters of phenological development in sugarcane, including suboptimum and supra-optimum temperature regimes. Agr. Forest Meteorol. 90:119–139.Google Scholar
Magarey, R. C., Yip, H. Y., Bull, J. I., and Johnson, E. J. 1997. Effect of the fungicide mancozeb on fungi associated with sugarcane yield decline in Queensland. Mycol. Res. 101:858–862.Google Scholar
Magurran, A. E. 1988. Ecological Diversity and its Measurement. London Croom Helm. 179 p.Google Scholar
Martínez-Ghersa, M. A., Ghersa, C. M., and Satorre, E. H. 2000. Coevolution of agricultural systems and their weed companions: implications for research. Field Crop. Res. 67:181–190.Google Scholar
McCune, B. and Mefford, M. J. 1995. PC-ORD: multivariate analysis of ecological data. Gleneden Beach, OR MjM Software Design.Google Scholar
McMahon, G. 1989. Weeds reduce cane yield in early growth stages. Brisbane, Australia Bureau of Sugar Experiment Station. Sugar Exp. Stn. Bull., 27 (July). Pages 21–32 Google Scholar
Muchow, R. C., Robertson, M. J., and Wood, A. W. 1996. Growth of sugarcane under high input conditions in tropical Australia. II. Sucrose accumulation and commercial yield. Field Crop. Res. 48:27–36.Google Scholar
Mueller-Dombois, D. and Ellenberg, H. 1974. Causal analytical inquiries into the origin of plant communities. Pages 335–370 in Aims and Methods of Vegetation Ecology. New York Wiley.Google Scholar
Pankhurst, C. E., Magarey, R. C., Stirling, G. R., Blair, B. L., Bell, M. J., and Garside, A. L. 2003. Management practices to improve soil health and reduce the effects of detrimental soil biota associated with yield decline of sugarcane in Queensland, Australia. Soil Till. Res. 72:125.Google Scholar
Pankhurst, C. E., Stirling, G. R., Magarey, R. C., Blair, B. L., Holt, J. A., Bell, M. J., and Garside, A. L. 2005. Quantification of the effects of rotation breaks on soil biological properties and their impact on yield decline in sugarcane. Soil Biol. BioChem. 37:1121–1130.Google Scholar
Peltzer, D. A., Ferriss, S., and FitzJohn, R. G. 2008. Predicting weed distribution at the landscape scale: using naturalized Brassica as a model system. J. Appl. Ecol. 45:467–475.Google Scholar
Peng, S. Y. 1984. The Biology and Control of Weeds in Sugarcane. New York Elsevier Science. 336 p.Google Scholar
Richard, E. P. Jr. 1995. Bermudagrass interference during a three year sugarcane crop cycle. Proc. Int. Soc. Sugar Cane Technol. 21:31–39.Google Scholar
Roel, A., Firpo, H., and Plant, R. E. 2007. Why do some farmers get higher yields? Multivariate analysis of a group of Uruguayan rice farmers. Comp. Electron. Agric. 58:78–92.Google Scholar
Russell, J. S., Wegener, M. K., and Valentine, T. R. 1991. Effect of weather variables on C.C.S. at Tully simulated by the AUSCANE model. Proc. Aust. Soc. Sugar Cane Technol. 13:157–163.Google Scholar
Sampietro, D. A., Vattuone, M. A., and Isla, M. I. 2006. Plant growth inhibitors isolated from sugarcane (Saccharum officinarum) straw. J. Plant Physiol. 163:837–846.Google Scholar
Shaw, P. J. 2003. Multivariate Statistics for the Environmental Sciences. New York. 233 p.Google Scholar
Smith, D. M., Inman-Bamber, N. G., and Thorburn, P. J. 2005. Growth and function of the sugarcane root system. Field Crop. Res. 92:169–183.Google Scholar
Smith, D. T. 1998. Weed Control in US Sugarcane. Technical Report 98-03. Texas: USDA Department of Soil and Crop Science, CollegeStation, TX: TexaS A&M University. Rep. 98-03.25 p.Google Scholar
Steinberg, D. and Colla, P. 1995. CART: Tree-Structured Non-Parametric Data Analysis. San Diego, CA Salford Systems. 336 p.Google Scholar
Ter Braak, C.J.F. and Prentice, C. 1988. A theory of gradient analysis. Adv. Ecol. Res. 18:271–317.Google Scholar
[USDA] U.S. Department of Agriculture. 1989. The Second RCA Appraisal: Soil, Water and Related Resources on Nonfederal Land in the United States. U.S. Department of Agriculture, Soil Conservation Service. 280 p.Google Scholar
Vallis, I., Parton, W. J., Keating, B. A., and Wood, A. W. 1996. Simulation of the effects of trash and N fertilizer management on soil organic matter levels and yields of sugarcane. Soil Till. Res. 38:115–132.Google Scholar
Waheed, T., Bonnell, R. B., Prasher, S. O., and Paulet, E. 2006. Measuring performance in precision agriculture: CART—a decision tree approach. Agric. Water Manag. 84:173–185.Google Scholar
Wiles, L. and Brodahl, M. 2004. Exploratory data analysis to identify factors influencing spatial distributions of weed seed banks. Weed Sci. 52:936–947.Google Scholar
Wood, W. 1991. Management of crop residues following green harvesting of sugarcane in North Queensland. Soil Till. Res. 20:69–85.Google Scholar